On Tue, 14 Oct 2008, Arnau Mir Torres wrote:
El 14/10/2008, a las 18:05, Martin Maechler escribió:
"AMT" == Arnau Mir Torres <[EMAIL PROTECTED]>
on Tue, 14 Oct 2008 17:13:01 +0200 writes:
"AMT" == Arnau Mir Torres <[EMAIL PROTECTED]>
on Tue, 14 Oct 2008 17:13:01 +0200 writes:
AMT> Hello.
AMT> I need to know how can R compute AIC when I study a regression
model?
Out of curiosity, why do you 'need' to know?
AMT> For example, if I use these data:
AMT> growth tannin
AMT> 1 12 0
AMT> 2 10 1
AMT> 3 8 2
AMT> 4 11 3
AMT> 5 6 4
AMT> 6 7 5
AMT> 7 2 6
AMT> 8 3 7
AMT> 9 3 8
AMT> and I do
AMT> model <- lm (growth ~ tannin)
AMT> AIC(model)
AMT> R responses:
AMT> 38.75990
AMT> I know the following formula to compute AIC:
AMT> AIC= -2*log-likelihood + 2*(p+1)
AMT> In my example, it would be:
AMT> AIC=-2*log-likelihood + 2*2
AMT> but I don't know how R computes log-likelihood:
AMT> logLik(model)
AMT> 'log Lik.' -16.37995 (df=3)
and so?
What is the formula to compute logLik? I don't know how to compute "by hand"
logLik(model) and obtain -16.37995.
Do you not have a regression textbook to look this up in? The R posting
guide makes quite clear this is not a list on basic statistics.
Hint: look at stats:::logLik.lm to see the formula expressed in R.
Arnau.
Hint: Your only problem is that your 'p' is wrongly off by one.
2nd Hint: sigma is a parameter, too
I suspect it is the intercept that was forgotten in p, and sigma is the
'1' in 'p+1'.
------------------------------------------------------------
Arnau Mir Torres
Edifici A. Turmeda
Campus UIB
Ctra. Valldemossa, km. 7,5
07122 Palma de Mca.
tel: (+34) 971172987
fax: (+34) 971173003
email: [EMAIL PROTECTED]
URL: http://dmi.uib.es/~arnau
------------------------------------------------------------
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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